Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python,Gensim,spaCy,and Keras
by: Bhargav Srivinasa-Desikan
ISBN-10: 178883853X
ISBN-13: 9781788838535
Publication Date 出版日期: 2018-06-29
Print Length 页数: 306
Book Description
By finelybook
Modern text analysis is now very accessible using Python and open source tools,so discover how you can now perform modern text analysis in this era of textual data.
This book shows you how to use natural language processing,and computational linguistics algorithms,to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now – with Python,and tools like Gensim and spaCy.
You’ll start by learning about data cleaning,and then how to perform computational linguistics from first concepts. You’re then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python,with realistic language and text samples. You’ll learn to tag,parse,and model text using the best tools. You’ll gain hands-on knowledge of the best frameworks to use,and you’ll know when to choose a tool like Gensim for topic models,and when to work with Keras for deep learning.
This book balances theory and practical hands-on examples,so you can learn about and conduct your own natural language processing projects and computational linguistics. You’ll discover the rich ecosystem of Python tools you have available to conduct NLP – and enter the interesting world of modern text analysis.
Contents
1: WHAT IS TEXT ANALYSIS?
2: PYTHON TIPS FOR TEXT ANALYSIS
3: SPACY’S LANGUAGE MODELS
4: GENSIM – VECTORIZING TEXT AND TRANSFORMATIONS AND N-GRAMS
5: POS-TAGGING AND ITS APPLICATIONS
6: NER-TAGGING AND ITS APPLICATIONS
7: DEPENDENCY PARSING
8: TOPIC MODELS
9: ADVANCED TOPIC MODELING
10: CLUSTERING AND CLASSIFYING TEXT
11: SIMILARITY QUERIES AND SUMMARIZATION
12: WORD2VEC,DOC2VEC,AND GENSIM
13: DEEP LEARNING FOR TEXT
14: KERAS AND SPACY FOR DEEP LEARNING
15: SENTIMENT ANALYSIS AND CHATBOTS
What You Will Learn
Why text analysis is important in our modern age
Understand NLP terminology and get to know the Python tools and datasets
Learn how to pre-process and clean textual data
Convert textual data into vector space representations
Using spaCy to process text
Train your own NLP models for computational linguistics
Use statistical learning and Topic Modeling algorithms for text,using Gensim and scikit-learn
Employ deep learning techniques for text analysis using Keras
Authors
Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille,France. He is a part of the MODAL (Models of Data Analysis and Learning) team,and he works on metric learning,predictor aggregation,and data visualization. He is a regular contributor to the Python open source community,and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia,and conducts tutorials on text analysis using Python.
